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dc.contributor.authorPham, XC
dc.contributor.authorNguyen, TTT
dc.contributor.authorLiew, AWC
dc.description.abstractIn this study, we introduce an online ensemble method based on convolutional neural networks (CNNs) for streaming data. Recent work has shown that a convolution operation has been an effective way to extract features. In particular, we proposed a CNN working in an online manner as a base classifier. Then, an ensemble approach is devised to boost the performance of all base classifiers. We also propose two loss terms which can adapt to the imbalanced data stream as well as handling the forgetting issue of deep networks. The experiments conducted on a number of datasets chosen from different sources demonstrate that the proposed ensemble approach performs significantly better than a single network and some well-known online learning algorithms including additive models and Online Bagging.
dc.relation.ispartofconferencename26th International Conference on Neural Information Processing (ICONIP 2019)
dc.relation.ispartofconferencetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartoflocationSydney, Australia
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.titleA novel online ensemble convolutional neural networks for streaming data
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationPham, XC; Nguyen, TTT; Liew, AWC, A novel online ensemble convolutional neural networks for streaming data, Neural Information Processing , 2019, 11953, pp. 199-210
gro.hasfulltextNo Full Text
gro.griffith.authorLiew, Alan Wee-Chung
gro.griffith.authorPham, Cuong X.
gro.griffith.authorNguyen, Thi Thu Thuy

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    Contains papers delivered by Griffith authors at national and international conferences.

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